Visualization of European basketball data

# Firstly, load BAwiR and other packages that will be used in the paper:
library(BAwiR)
library(tidyverse)
library(Anthropometry)

# Code for Figure 1:
# Load the data_app_acb file with the ACB games from seasons 1985-1986 to 2017-2018:
load(url("http://www.uv.es/vivigui/softw/data_app_acb.RData"))
get_pop_pyramid_acb(data_app_acb)

# Create the data with games and players' info, add the advanced stats 
# and compute the total numbers:
df0 <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718)
df1 <- do_add_adv_stats(df0)
df2 <- do_stats(df1, "Total", "2017-2018", "ACB", "Regular Season")

# Code for Table 2:
df3 <- df2[which(df2$Position == "Center"), c("MP", "PTS", "Name")]
preproc <- preprocessing(df3[,1:2], stand = TRUE, percAccomm = 1)
set.seed(4321)
lass <- stepArchetypesRawData(preproc$data, 1:2, numRep = 20, verbose = FALSE)
res <- archetypoids(2, preproc$data, huge = 200, step = FALSE, ArchObj = lass, 
                    nearest = "cand_ns", sequ = TRUE)
cases <- anthrCases(res)
alphas <- round(res$alphas, 4)
df3[cases,]

# Code for Figure 2:
sel1 <- df3 %>% 
   mutate(Archetypoid = ifelse(as.numeric(rownames(df3)) %in% cases, "Yes", "No"))
ggplot(sel1, aes(x = c(sel1[,1])[[1]], y = c(sel1[,2])[[1]], color = Archetypoid)) +
  geom_point() +
  labs(x = colnames(sel1)[1], y = colnames(sel1)[2]) +
  guides(color = guide_legend(override.aes = list(size = 5)))

# Code for Table 3:
df3_aux <- df2[which(df2$Position == "Center"), ]
get_similar_players(2, 0.95, alphas, cases, df3_aux, c("MP", "PTS"), 
                    unique(df3_aux$Compet), unique(df3_aux$Season))

# Code for Figure 3:
stats <- c("GP", "MP", "PTS", "FGPerc", "FTPerc", "TRB", "AST", "TOV", "PlusMinus", "PIR")
descr_stats <- c("Games played", "Minutes played", "Points", "Field goals percentage", 
                 "Free throws percentage", "Total rebounds", "Assists", "Turnovers", 
                 "Plus/minus", "Performance index rating")
df2_1 <- df2 %>% 
  select(1:5, stats, 46:49)
get_bubble_plot(df2_1, "Doncic, Luka", descr_stats)

# Code for Figure 4:
months <- c(df0 %>% distinct(Month))$Month
months_order <- c("September", "October", "November", "December",  "January", 
                  "February", "March", "April", "May", "June")
months_plot <- match(months_order, months)
months_plot1 <- months_plot[!is.na(months_plot)]
months_plot2 <- months[months_plot1]

df1_m <- df1 %>%
              filter(Team == "Real_Madrid", 
                     Player.x == "Doncic, Luka") %>%
              group_by(Month) %>%
              do(do_stats(., "Average", "2017-2018", "ACB", "Regular Season")) %>%
              ungroup() %>%
              mutate(Month = factor(Month, levels = months_plot2)) %>%
              arrange(Month)


df1_m1 <- df1_m %>% 
  select(1:5, stats, 46:50) %>% 
  select(-EPS)
title <- paste(paste(";", "ACB"), "2017-2018", "Regular Season", 
                         "Average", sep = " ; ")
get_barplot_monthly_stats(df1_m1, title, 2, 4)

# Code for Figure 5:
df0$Compet <- "ACB"
plot_yearly <- get_stats_seasons(df0, "ACB", "Doncic, Luka", stats, "Regular Season")
plot_yearly$gg + labs(title = "Doncic, Luka ; Yearly statistics")

# Code for Figure 6:
levels_stats <- list("Offensive" = c("PTS", "FG", "FGA", "FGPerc", 
                                     "TwoP", "TwoPA", "TwoPPerc",
                                     "ThreeP", "ThreePA", "ThreePPerc",
                                     "FT", "FTA", "FTPerc", "ORB", "AST"),
                     "Defensive" = c("DRB", "STL", "PF"),
                     "Other" = c("GP", "MP", "TRB", "PlusMinus", "PIR"),
                     "Advanced" = c("EFGPerc", "PPS"))
get_heatmap_bb(df2, "Real_Madrid", levels_stats, "PlusMinus", 9, 
               paste("ACB", "2017-2018", "Total", sep = " "))

# Code for Figure 7:
get_shooting_plot(df2, "Real_Madrid", 3, 1, paste("ACB", "17-18", sep = " "), "en")

# Code for Figure 8:
df1_10 <- df1 %>% 
  filter(Day <= 10) 
teams <- as.character(rev(sort(unique(df2$Team))))
df_four_factors <- do_four_factors_df(df1_10, teams)
get_four_factors_plot(df_four_factors$df_rank, df_four_factors$df_no_rank, "Real_Madrid", "en")

# Code for Figure 9:
df0$Compet <- "ACB"
gg <- get_table_results(df0, "ACB", "2017-2018")
gg$plot_teams

# Code for Figure 10:
get_map_nats(df2)

sessionInfo()